Cheng Yanda, Zheng Wenhan, Bing Robert, Zhang Huijuan, Huang Chuqin, Huang Peizhou, Ying Leslie, Xia Jun
Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, USA.
Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, USA.
Biomed Opt Express. 2024 Jul 2;15(8):4390-4405. doi: 10.1364/BOE.529253. eCollection 2024 Aug 1.
In this study, we implemented an unsupervised deep learning method, the Noise2Noise network, for the improvement of linear-array-based photoacoustic (PA) imaging. Unlike supervised learning, which requires a noise-free ground truth, the Noise2Noise network can learn noise patterns from a pair of noisy images. This is particularly important for in vivo PA imaging, where the ground truth is not available. In this study, we developed a method to generate noise pairs from a single set of PA images and verified our approach through simulation and experimental studies. Our results reveal that the method can effectively remove noise, improve signal-to-noise ratio, and enhance vascular structures at deeper depths. The denoised images show clear and detailed vascular structure at different depths, providing valuable insights for preclinical research and potential clinical applications.
在本研究中，我们实施了一种无监督深度学习方法——噪声到噪声网络，以改进基于线性阵列的光声（PA）成像。与需要无噪声真实图像的监督学习不同，噪声到噪声网络可以从一对有噪声的图像中学习噪声模式。这对于无法获得真实图像的体内光声成像尤为重要。在本研究中，我们开发了一种从单组光声图像生成噪声对的方法，并通过模拟和实验研究验证了我们的方法。我们的结果表明，该方法可以有效去除噪声、提高信噪比并增强更深层的血管结构。去噪后的图像在不同深度显示出清晰且详细的血管结构，为临床前研究和潜在的临床应用提供了有价值的见解。
